6 papers
CoAX: Cognitive-Oriented Attribution eXplanation User Model of Human Understanding of AI Explanations
Louth Bin Rawshan, Zhuoyu Wang, Brian Y. Lim
Explainable AI (XAI) aims to improve user understanding and decisions when using AI models. However, despite innovations in XAI, recent user evaluations reveal that this goal remai…
From Control to Foresight: Simulation as a New Paradigm for Human-Agent Collaboration
Gaole He, Brian Y. Lim
Large Language Models (LLMs) are increasingly used to power autonomous agents for complex, multi-step tasks. However, human-agent interaction remains pointwise and reactive: users…
Beyond Scores: Explainable Intelligent Assessment Strengthens Pre-service Teachers' Assessment Literacy
Yuang Wei, Fei Wang, Yifan Zhang +2
Assessment literacy (AL) is essential for personalized education, yet difficult to cultivate in pre-service teachers. Conventional teacher preparation programs focus on theoretical…
Rules or Weights? Comparing User Understanding of Explainable AI Techniques with the Cognitive XAI-Adaptive Model
Louth Bin Rawshan, Zhuoyu Wang, Brian Y Lim
Rules and Weights are popular XAI techniques for explaining AI decisions. Yet, it remains unclear how to choose between them, lacking a cognitive framework to compare their interpr…
iRULER: Intelligible Rubric-Based User-Defined LLM Evaluation for Revision
Jingwen Bai, Wei Soon Cheong, Philippe Muller +1
Large Language Models (LLMs) have become indispensable for evaluating writing. However, text feedback they provide is often unintelligible, generic, and not specific to user criter…
Editable XAI: Toward Bidirectional Human-AI Alignment with Co-Editable Explanations of Interpretable Attributes
Haoyang Chen, Jingwen Bai, Fang Tian +1
While Explainable AI (XAI) helps users understand AI decisions, misalignment in domain knowledge can lead to disagreement. This inconsistency hinders understanding, and because exp…